Microsoft announces container support for Azure Cognitive Services to build intelligent applications that span the cloud and the edge

On Wednesday, Microsoft announced the preview of Azure Cognitive Services containers, which will make it possible to build intelligent applications that span the cloud and the edge. Azure Cognitive Services allows developers to easily add cognitive features such as object detection, vision recognition, and language understanding into their applications.

With containerization, developers are able to build large AI systems that are scalable, reliable, and consistent in a way that supports better data governance. It is a way of software distribution in which an application or service is packaged so that it can be deployed in a container host with little or no modification.

Following are the advantages of container support for Azure Cognitive Services:

Build portable and scalable intelligent apps

Containerisation will allow customers to use Azure Cognitive Services capabilities wherever the data resides. The applications will be able to perform functionalities like facial recognition, OCR, or text analytics without sending data to the cloud. Irrespective of where the apps are running (edge or in Azure), they will be portable and scalable with great consistency.

Flexibility to deploy AI capabilities

Everyday, huge volumes of data are generated across organizations, which demands a flexible way to deploy AI capabilities in a variety of environments. Deploying Cognitive Services in containers allows customers to analyze information close to the physical world where the data resides. This helps in delivering real-time insights and immersive experiences that are highly responsive and contextually aware.

Build one app architecture optimized for both cloud and edge

Container support for Cognitive Services allows customers to build one application architecture that is optimized to take advantage of both robust cloud capabilities and edge locality. Customers can now choose when to upgrade the AI models deployed in their solutions. They can also test new model version before deploying them in production in a consistent way, whether running on the edge or in Azure.

“Azure Cognitive Services containers give you more options on how you grow and deploy AI solutions, either on or off premises, with consistent performance. You can scale up as workload intensity increases or scale out to the edge.”